Scaling a slow-wave sleep cortical network model using NEOSIM

Abstract

We describe a case study transforming a simulation model coded in sequential C++ to run in parallel under Neosim, to enable much larger compartmental network models to be run. For some network models cut down scale is sufficient; however, there are cases where network behaviour cannot be reproduced on a smaller model (e.g. Neurocomputing 32–33 (2000) 1041). The example we present is a model of slow-wave sleep oscillations. In an earlier paper (Neurocomputing 38 (2001) 1657) we outlined the design of the Neosim framework for scaling models, focussing on networks of compartmental neuron models built using existing simulation tools Neuron and Genesis. Here, we explain how a Hodgkin–Huxley network model coded in C++ for a cortical network was adapted for Neosim, and describe the experiments planned. This case study should be of interest to others considering how best to scale up existing models and interface their own coded models with other simulators.